TY - CHAP A1 - Baumann, Timo A1 - Siegert, Ingo ED - Alt, Florian ED - Schneegass, Stefan ED - Hornecker, Eva T1 - Prosodic addressee-detection : ensuring privacy in always-on spoken dialog systems T2 - Tagungsband Mensch und Computer (MuC'20): 06.09.2020 - 09.09.2020, Magdeburg N2 - We analyze the addressee detection task for complexity-identical dialog for both human conversation and device-directed speech. Our recurrent neural model performs at least as good as humans, who have problems with this task, even native speakers, who profit from the relevant linguistic skills. We perform ablation experiments on the features used by our model and show that fundamental frequency variation is the single most relevant feature class. Therefore, we conclude that future systems can detect whether they are addressed based only on speech prosody which does not (or only to a very limited extent) reveal the content of conversations not intended for the system. KW - addressee detection KW - complexity-identical human-computer interaction KW - computational paralinguistics KW - fundamental frequency variation KW - recurrent neural network Y1 - 2020 SN - 9781450375405 U6 - https://doi.org/10.1145/3404983.3410021 SP - 195 EP - 198 PB - Association for Computing Machinery CY - New York, NY, United States ER -